Pupil-teacher ratio, tertiary

Source: worldbank.org, 03.09.2025

Year: 2018

Flag Country Value Value change, % Rank
Afghanistan Afghanistan 21.5 -9.9% 17
Albania Albania 14.1 -8.24% 33
Andorra Andorra 4.43 +13.4% 53
Armenia Armenia 6.92 -0.193% 48
Azerbaijan Azerbaijan 9.92 -1.02% 43
Burkina Faso Burkina Faso 17.3 -11.1% 24
Bangladesh Bangladesh 37.1 +22.3% 1
Bahrain Bahrain 21.9 +12.7% 16
Bosnia & Herzegovina Bosnia & Herzegovina 9.37 -8.05% 44
Belarus Belarus 13.9 -3.86% 35
Bermuda Bermuda 14.5 +22.7% 31
Brunei Brunei 12.4 +1.87% 38
Bhutan Bhutan 15.6 +101% 26
Colombia Colombia 14.8 -5.22% 29
Cape Verde Cape Verde 7.98 -9.08% 46
Cuba Cuba 4.79 +4.51% 52
Algeria Algeria 27.3 +1.77% 8
Georgia Georgia 8.33 +13.8% 45
Ghana Ghana 26.5 -2.75% 10
Grenada Grenada 5.71 +392% 49
Honduras Honduras 21.5 -13% 18
Indonesia Indonesia 27.3 -3.69% 9
India India 24.7 +1.19% 13
Kazakhstan Kazakhstan 16.5 +23.1% 25
Kyrgyzstan Kyrgyzstan 11.1 -12.6% 41
Laos Laos 11.3 -5.58% 40
St. Lucia St. Lucia 7.71 -27.1% 47
Sri Lanka Sri Lanka 29.8 -3.9% 4
Lesotho Lesotho 19.7 -1.4% 20
Macao SAR China Macao SAR China 14.4 -0.605% 32
Morocco Morocco 28.7 -0.362% 5
Moldova Moldova 13.8 -7.41% 36
Madagascar Madagascar 24.8 +4.79% 11
Myanmar (Burma) Myanmar (Burma) 35.5 +40.5% 2
Montenegro Montenegro 14.9 -4.39% 28
Mongolia Mongolia 23.1 +22.2% 14
Mozambique Mozambique 15.2 +7.61% 27
Malaysia Malaysia 13.3 -4.01% 37
Niger Niger 14.7 -3.87% 30
Pakistan Pakistan 33 -0.122% 3
Palestinian Territories Palestinian Territories 27.9 +4.17% 7
Qatar Qatar 14 +3.56% 34
Rwanda Rwanda 24.7 +5.62% 12
Saudi Arabia Saudi Arabia 19 -5.32% 21
Senegal Senegal 27.9 +38.5% 6
El Salvador El Salvador 18 +1.8% 22
San Marino San Marino 5.1 51
Serbia Serbia 22.1 -3.15% 15
Seychelles Seychelles 17.5 -17.4% 23
Togo Togo 21.3 +9.32% 19
Ukraine Ukraine 10.3 -1.06% 42
Uzbekistan Uzbekistan 11.9 +1.6% 39
British Virgin Islands British Virgin Islands 5.44 -41.3% 50

The pupil-teacher ratio in tertiary education serves as a critical indicator of educational quality and access. It reflects the number of students enrolled per teacher in educational institutions that provide advanced education beyond secondary school. A lower ratio generally implies smaller class sizes, which can enhance the quality of education received by students. Conversely, a higher ratio may indicate overcrowded classrooms, making it difficult for teachers to provide personalized attention and support to individual students.

Understanding the pupil-teacher ratio is paramount, as it directly correlates with various dimensions of educational and societal development. It can be seen as a proxy for the quality of educational resources available, which can impact student outcomes in terms of learning, retention, and graduation rates. For policymakers and educational planners, monitoring this ratio is vital to ensure that educational institutions can provide effective instruction and maintain standards that enable students to succeed.

In 2019, the median pupil-teacher ratio in tertiary education stood at 11.26. This number indicates a balanced distribution of students to teachers, suggesting that on average, students have access to relatively good educational support. However, a more nuanced examination reveals varying scenarios in different countries and regions. For example, Kazakhstan reported a pupil-teacher ratio of 15.07, indicating a more crowded educational setting. This suggests that educators may struggle to maintain the necessary attention and support for students in Kazakhstan, potentially affecting academic performance and engagement in tertiary institutions.

On the other hand, Georgia presented a significantly lower pupil-teacher ratio of 7.44. This figure suggests a more favorable learning environment, where students may benefit from closer interactions with their educators, leading to enriched educational experiences and better academic outcomes. However, this context should be understood within the broader framework of educational policies, funding, and societal support for education. Higher ratios in specific regions often reflect historical issues, such as underfunding and lack of trained personnel in the education system.

Moreover, the pupil-teacher ratio relates closely to other educational indicators such as enrollment rates, graduation rates, and educational expenditure. The enrollment rate is directly impacted by the availability of qualified teachers; lack of resources can deter potential students from pursuing higher education. Furthermore, a high pupil-teacher ratio may lead to lower graduation rates as students may not receive the individual attention necessary to navigate their academic journeys successfully. Therefore, to improve educational outcomes, it is fundamental for countries with high ratios to strategize on how to allocate resources effectively to reduce this ratio.

Several factors influence the pupil-teacher ratio in tertiary education, including government policies, funding for education, and availability of teaching staff. Investment in education is crucial, as financial support can determine the number of teachers hired and trained, impacting not just the ratios but also the quality of education provided. Countries facing economic challenges often struggle to recruit and retain qualified educators, exacerbating high pupil-teacher ratios. Thus, a moment of economic hardship can have long-term repercussions on the educational landscape.

Strategically, countries can address unfavorable pupil-teacher ratios through various approaches. For instance, incentivizing teacher training programs can foster a more robust pipeline of qualified professionals entering the education sector. This can involve scholarships for education majors, partnering with universities to provide on-the-job training, or implementing mentorship programs that equip new educators with valuable skills. Additionally, leveraging technology to facilitate distance learning can provide alternative educational avenues, thereby reducing the strain on traditional classroom settings.

Solutions to high pupil-teacher ratios may also involve policy reforms at the national and local levels. This might include revisiting funding allocation within the education sector to ensure that resources are directed toward schools with the highest needs or investing in professional development for current educators to optimize their effectiveness in larger classrooms. Moreover, engaging communities in educational planning can result in tailored approaches to local challenges, ensuring that decisions reflect the specific context and needs of each area's educational system.

However, the approaches to managing pupil-teacher ratios are not without flaws. Policies aimed at immediate reductions in ratios may overlook the importance of educational quality overall. For example, simply hiring more teachers does not guarantee better educational outcomes if those teachers lack training or resources. Additionally, the implementation of such policies can vary widely across regions, leading to disparities in educational quality that persist despite attempts to evenly distribute teacher resources.

In conclusion, while a median pupil-teacher ratio of 11.26 portrays a relatively favorable landscape for tertiary education, the context of individual countries like Kazakhstan and Georgia highlights the critical need for targeted strategies to address disparities and enhance educational quality. Understanding the intricate relationships between this indicator and various factors—from economic conditions to systemic policies—enables stakeholders in the education sector to formulate comprehensive approaches that prioritize effective teaching and learning environments, ensuring that students can thrive academically in their pursuit of higher education.

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'SE.TER.ENRL.TC.ZS'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                
                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'SE.TER.ENRL.TC.ZS'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))